Unsupervised Sentiment Analysis for Code-mixed Data

• URL: http://arxiv.org/abs/2001.11384v1
• Date: Mon, 20 Jan 2020 06:12:12 GMT
• Title: Unsupervised Sentiment Analysis for Code-mixed Data
• Authors: Siddharth Yadav, Tanmoy Chakraborty
• Abstract summary: We introduce methods that use different kinds of multilingual and cross-lingual embeddings to efficiently transfer knowledge from monolingual text to code-mixed text. Our methods beat state-of-the-art on English-Spanish code-mixed sentiment analysis by absolute 3% F1-score.
• Score: 33.939487457110566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code-mixing is the practice of alternating between two or more languages. Mostly observed in multilingual societies, its occurrence is increasing and therefore its importance. A major part of sentiment analysis research has been monolingual, and most of them perform poorly on code-mixed text. In this work, we introduce methods that use different kinds of multilingual and cross-lingual embeddings to efficiently transfer knowledge from monolingual text to code-mixed text for sentiment analysis of code-mixed text. Our methods can handle code-mixed text through a zero-shot learning. Our methods beat state-of-the-art on English-Spanish code-mixed sentiment analysis by absolute 3\% F1-score. We are able to achieve 0.58 F1-score (without parallel corpus) and 0.62 F1-score (with parallel corpus) on the same benchmark in a zero-shot way as compared to 0.68 F1-score in supervised settings. Our code is publicly available.

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